{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction: Feature Selection Class\n",
    "\n",
    "In this notebook we will develop a Python class for feature detection. The objective is to create a an object that encompasses several different feature selection methods and that can be applied to multiple datasets. This class will implement five different feature selection methods:\n",
    "\n",
    "1. Remove columns with a missing fraction greater than a specified threshold\n",
    "2. Remove features with only a single unique value\n",
    "3. Remove collinear features as idenfitied by a correlation coefficient greater than a specified value\n",
    "4. Remove features with 0.0 importance from a gradient boosting machine\n",
    "5. Remove features that do not contribute to a specified cumulative feature importance from the gradient boosting machine\n",
    "\n",
    "This notebook is focused more on development. See the Feature Selector Usage notebook for a more cohesive implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import gc\n",
    "\n",
    "from sklearn.exceptions import NotFittedError\n",
    "\n",
    "from itertools import chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/application_train.csv')\n",
    "train = train.sample(10000)\n",
    "train_labels = train['TARGET']\n",
    "train = train.drop(columns = 'TARGET')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import lightgbm as lgb\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import gc\n",
    "\n",
    "from sklearn.exceptions import NotFittedError\n",
    "\n",
    "from itertools import chain\n",
    "\n",
    "class FeatureSelector():\n",
    "    \"\"\"\n",
    "    Class for performing feature selection for machine learning or data preprocessing.\n",
    "    \n",
    "    Implements five different methods\n",
    "    \n",
    "        1. Remove columns with a missing percentage greater than a specified threshold\n",
    "        2. Remove columns with a single unique value\n",
    "        3. Remove collinear variables with a correlation greater than a specified correlation coefficient\n",
    "        4. Remove features with 0.0 feature importance from a gradient boosting machine (gbm)\n",
    "        5. Remove features that do not contribute to a specified cumulative feature importance from the gbm\n",
    "        \n",
    "    Attributes\n",
    "    --------\n",
    "    \n",
    "    record_missing : dataframe\n",
    "        Records the fraction of missing values for features with missing fraction above threshold\n",
    "    \n",
    "    record_single_unique : dataframe\n",
    "        Records the features that have a single unique value\n",
    "    \n",
    "    record_collinear : dataframe\n",
    "        Records the pairs of collinear variables with a correlation coefficient above the threshold\n",
    "    \n",
    "    record_zero_importance : dataframe\n",
    "        Records the zero importance features in the data according to the gbm\n",
    "    \n",
    "    record_low_importance : dataframe\n",
    "        Records the lowest importance features not needed to reach the threshold of cumulative importance according to the gbm\n",
    "    \n",
    "    feature_importances : dataframe\n",
    "        All the features importances from the gbm\n",
    "    \n",
    "    removal_ops : dict\n",
    "        Dictionary of removal operations and associated features for removal identified\n",
    "        \n",
    "    Notes\n",
    "    --------\n",
    "    \n",
    "        - All 5 operations can be run with the `identify_all` method.\n",
    "        - Calculating the feature importances requires labels (a supervised learning task) \n",
    "          for training the gradient boosting machine\n",
    "        - For the feature importances, the dataframe is first one-hot encoded before training the gbm.\n",
    "    \n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self):\n",
    "        \n",
    "        # Dataframes recording information about features to remove\n",
    "        self.record_missing = None\n",
    "        self.record_single_unique = None\n",
    "        self.record_collinear = None\n",
    "        self.record_zero_importance = None\n",
    "        self.record_low_importance = None\n",
    "        \n",
    "        self.feature_importances = None\n",
    "        \n",
    "        # Dictionary to hold removal operations\n",
    "        self.removal_ops = {}\n",
    "        \n",
    "        \n",
    "        \n",
    "    def identify_missing(self, data, missing_threshold):\n",
    "        \"\"\"Find the features with a fraction of missing values above `missing_threshold`\"\"\"\n",
    "        \n",
    "        self.missing_threshold = missing_threshold\n",
    "\n",
    "        # Calculate the fraction of missing in each column \n",
    "        missing_series = data.isnull().sum() / data.shape[0]\n",
    "        \n",
    "        self.missing_stats = pd.DataFrame(missing_series).rename(columns = {'index': 'feature', 0: 'missing_fraction'})\n",
    "\n",
    "        # Find the columns with a missing percentage above the threshold\n",
    "        record_missing = pd.DataFrame(missing_series[missing_series > missing_threshold]).reset_index().rename(columns = {'index': 'feature', 0: 'missing_fraction'})\n",
    "\n",
    "        to_drop = list(record_missing['feature'])\n",
    "\n",
    "        self.record_missing = record_missing\n",
    "        self.removal_ops['missing'] = to_drop\n",
    "        \n",
    "        print('%d features with greater than %0.2f missing values.\\n' % (len(self.removal_ops['missing']), self.missing_threshold))\n",
    "        \n",
    "    def identify_single_unique(self, data):\n",
    "        \"\"\"Identifies features with only a single unique value. NaNs do not count as a unique value. \"\"\"\n",
    "\n",
    "        # Calculate the unique counts in each column\n",
    "        unique_counts = data.nunique()\n",
    "\n",
    "        self.unique_stats = pd.DataFrame(unique_counts).rename(columns = {'index': 'feature', 0: 'nunique'})\n",
    "        \n",
    "        # Find the columns with only one unique count\n",
    "        record_single_unique = pd.DataFrame(unique_counts[unique_counts == 1]).reset_index().rename(columns = {'index': 'feature', 0: 'nunique'})\n",
    "\n",
    "        to_drop = list(record_single_unique['feature'])\n",
    "    \n",
    "        self.record_single_unique = record_single_unique\n",
    "        self.removal_ops['single_unique'] = to_drop\n",
    "        \n",
    "        print('%d features with a single unique value.\\n' % len(self.removal_ops['single_unique']))\n",
    "    \n",
    "    def identify_collinear(self, data, correlation_threshold):\n",
    "        \"\"\"\n",
    "        Finds collinear features based on the correlation coefficient between features. \n",
    "        For each pair of features with a correlation coefficient greather than `correlation_threshold`,\n",
    "        only one of the pair is identified for removal. \n",
    "\n",
    "        Using code adapted from: https://gist.github.com/Swarchal/e29a3a1113403710b6850590641f046c\n",
    "        \n",
    "        Parameters\n",
    "        --------\n",
    "\n",
    "        data : dataframe\n",
    "            Data observations in the rows and features in the columns\n",
    "\n",
    "        correlation_threshold : float between 0 and 1\n",
    "            Value of the Pearson correlation cofficient for identifying correlation features\n",
    "\n",
    "        \"\"\"\n",
    "        \n",
    "        self.correlation_threshold = correlation_threshold\n",
    "\n",
    "        # Calculate the correlations between every column\n",
    "        corr_matrix = data.corr()\n",
    "        \n",
    "        self.corr_matrix = corr_matrix\n",
    "    \n",
    "        # Extract the upper triangle of the correlation matrix\n",
    "        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k = 1).astype(np.bool))\n",
    "        \n",
    "        # Select the features with correlations above the threshold\n",
    "        # Need to use the absolute value\n",
    "        to_drop = [column for column in upper.columns if any(upper[column].abs() > correlation_threshold)]\n",
    "\n",
    "        # Dataframe to hold correlated pairs\n",
    "        record_collinear = pd.DataFrame(columns = ['drop_feature', 'corr_feature', 'corr_value'])\n",
    "\n",
    "        # Iterate through the columns to drop\n",
    "        for column in to_drop:\n",
    "\n",
    "            # Find the correlated features\n",
    "            corr_features = list(upper.index[upper[column].abs() > correlation_threshold])\n",
    "\n",
    "            # Find the correlated values\n",
    "            corr_values = list(upper[column][upper[column].abs() > correlation_threshold])\n",
    "            drop_features = [column for _ in range(len(corr_features))]    \n",
    "\n",
    "            # Record the information (need a temp df for now)\n",
    "            temp_df = pd.DataFrame.from_dict({'drop_feature': drop_features,\n",
    "                                             'corr_feature': corr_features,\n",
    "                                             'corr_value': corr_values})\n",
    "\n",
    "            # Add to dataframe\n",
    "            record_collinear = record_collinear.append(temp_df, ignore_index = True)\n",
    "\n",
    "            \n",
    "        self.record_collinear = record_collinear\n",
    "        self.removal_ops['collinear'] = to_drop\n",
    "        \n",
    "        print('%d features with a correlation greater than %0.2f.\\n' % (len(self.removal_ops['collinear']), self.correlation_threshold))\n",
    "\n",
    "    def identify_zero_importance(self, features, labels, eval_metric, task='classification', \n",
    "                                 n_iterations=10, early_stopping = True):\n",
    "        \"\"\"\n",
    "        \n",
    "        Identify the features with zero importance according to a gradient boosting machine.\n",
    "        The gbm can be trained with early stopping using a validation set to prevent overfitting. \n",
    "        The feature importances are averaged over n_iterations to reduce variance. \n",
    "        \n",
    "        Uses the LightGBM implementation (http://lightgbm.readthedocs.io/en/latest/index.html)\n",
    "\n",
    "        Parameters \n",
    "        --------\n",
    "        features : dataframe\n",
    "            Data for training the model with observations in the rows\n",
    "            and features in the columns\n",
    "\n",
    "        labels : array, shape = (1, )\n",
    "            Array of labels for training the model. These can be either binary \n",
    "            (if task is 'classification') or continuous (if task is 'regression')\n",
    "\n",
    "        eval_metric : string\n",
    "            Evaluation metric to use for the gradient boosting machine\n",
    "\n",
    "        task : string, default = 'classification'\n",
    "            The machine learning task, either 'classification' or 'regression'\n",
    "\n",
    "        n_iterations : int, default = 10\n",
    "            Number of iterations to train the gradient boosting machine\n",
    "            \n",
    "        early_stopping : boolean, default = True\n",
    "            Whether or not to use early stopping with a validation set when training\n",
    "        \n",
    "        \n",
    "        Notes\n",
    "        --------\n",
    "        \n",
    "        - Features are one-hot encoded to handle the categorical variables before training.\n",
    "        - The gbm is not optimized for any particular task and might need some hyperparameter tuning\n",
    "        - Feature importances, including zero importance features, can change across runs\n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        # One hot encoding\n",
    "        features = pd.get_dummies(features)\n",
    "\n",
    "        # Extract feature names\n",
    "        feature_names = list(features.columns)\n",
    "\n",
    "        # Convert to np array\n",
    "        features = np.array(features)\n",
    "        labels = np.array(labels).reshape((-1, ))\n",
    "\n",
    "        # Empty array for feature importances\n",
    "        feature_importance_values = np.zeros(len(feature_names))\n",
    "        \n",
    "        print('Training Gradient Boosting Model\\n')\n",
    "        \n",
    "        # Iterate through each fold\n",
    "        for _ in range(n_iterations):\n",
    "\n",
    "            if task == 'classification':\n",
    "                model = lgb.LGBMClassifier(n_estimators=1000, learning_rate = 0.05, verbose = -1)\n",
    "\n",
    "            elif task == 'regression':\n",
    "                model = lgb.LGBMRegressor(n_estimators=1000, learning_rate = 0.05, verbose = -1)\n",
    "\n",
    "            else:\n",
    "                raise ValueError('Task must be either \"classification\" or \"regression\"')\n",
    "                \n",
    "            # If training using early stopping need a validation set\n",
    "            if early_stopping:\n",
    "                \n",
    "                train_features, valid_features, train_labels, valid_labels = train_test_split(features, labels, test_size = 0.15)\n",
    "\n",
    "                # Train the model with early stopping\n",
    "                model.fit(train_features, train_labels, eval_metric = eval_metric,\n",
    "                          eval_set = [(valid_features, valid_labels)],\n",
    "                          early_stopping_rounds = 100, verbose = -1)\n",
    "                \n",
    "                # Clean up memory\n",
    "                gc.enable()\n",
    "                del train_features, train_labels, valid_features, valid_labels\n",
    "                gc.collect()\n",
    "                \n",
    "            else:\n",
    "                model.fit(features, labels)\n",
    "\n",
    "            # Record the feature importances\n",
    "            feature_importance_values += model.feature_importances_ / n_iterations\n",
    "\n",
    "        feature_importances = pd.DataFrame({'feature': feature_names, 'importance': feature_importance_values})\n",
    "\n",
    "        # Sort features according to importance\n",
    "        feature_importances = feature_importances.sort_values('importance', ascending = False).reset_index(drop = True)\n",
    "\n",
    "        # Normalize the feature importances to add up to one\n",
    "        feature_importances['normalized_importance'] = feature_importances['importance'] / feature_importances['importance'].sum()\n",
    "        feature_importances['cumulative_importance'] = np.cumsum(feature_importances['normalized_importance'])\n",
    "\n",
    "        # Extract the features with zero importance\n",
    "        record_zero_importance = feature_importances[feature_importances['importance'] == 0.0]\n",
    "        \n",
    "        to_drop = list(record_zero_importance['feature'])\n",
    "\n",
    "        self.feature_importances = feature_importances\n",
    "        self.record_zero_importance = record_zero_importance\n",
    "        self.removal_ops['zero_importance'] = to_drop\n",
    "        \n",
    "        print('\\n%d features with zero importance.\\n' % len(self.removal_ops['zero_importance']))\n",
    "    \n",
    "    def identify_low_importance(self, cumulative_importance):\n",
    "        \"\"\"\n",
    "        Finds the lowest importance features not needed to account for `cumulative_importance` \n",
    "        of the feature importance from the gradient boosting machine. As an example, if cumulative\n",
    "        importance is set to 0.95, this will retain only the most important features needed to \n",
    "        reach 95% of the total feature importance. The identified features are those not needed.\n",
    "\n",
    "        Parameters\n",
    "        --------\n",
    "        cumulative_importance : float between 0 and 1\n",
    "            The fraction of cumulative importance to account for \n",
    "\n",
    "        \"\"\"\n",
    "\n",
    "        self.cumulative_importance = cumulative_importance\n",
    "        \n",
    "        # The feature importances need to be calculated before running\n",
    "        if self.feature_importances is None:\n",
    "            raise NotFittedError('Feature importances have not yet been determined. Call the `identify_zero_importance` method` first.')\n",
    "            \n",
    "        # Make sure most important features are on top\n",
    "        self.feature_importances = self.feature_importances.sort_values('cumulative_importance')\n",
    "\n",
    "        # Identify the features not needed to reach the cumulative_importance\n",
    "        record_low_importance = self.feature_importances[self.feature_importances['cumulative_importance'] > cumulative_importance]\n",
    "\n",
    "        to_drop = list(record_low_importance['feature'])\n",
    "\n",
    "        self.record_low_importance = record_low_importance\n",
    "        self.removal_ops['low_importance'] = to_drop\n",
    "    \n",
    "        print('%d features that do not contribute to cumulative importance of %0.2f.\\n' % (len(self.removal_ops['low_importance']), self.cumulative_importance))\n",
    "        \n",
    "    def identify_all(self, features, labels, selection_params):\n",
    "        \"\"\"\n",
    "        Use all five of the methods to identify features to remove.\n",
    "        \n",
    "        Parameters\n",
    "        --------\n",
    "        \n",
    "        features : dataframe\n",
    "            Data for training the model with observations in the rows\n",
    "            and features in the columns\n",
    "\n",
    "        labels : array, shape = (1, )\n",
    "            Array of labels for training the model. These can be either binary \n",
    "            (if task is 'classification') or continuous (if task is 'regression')\n",
    "            \n",
    "        selection_params : dict\n",
    "           Parameters to use in the five feature selection methhods.\n",
    "           Params must contain the keys ['missing_threshold', 'correlation_threshold', 'eval_metric', 'task', 'cumulative_importance']\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        # Check for all required parameters\n",
    "        for param in ['missing_threshold', 'correlation_threshold', 'eval_metric', 'task', 'cumulative_importance']:\n",
    "            if param not in selection_params.keys():\n",
    "                raise ValueError('%s is a required parameter for this method' % param)\n",
    "        \n",
    "        # Implement each of the five methods\n",
    "        self.identify_missing(features, selection_params['missing_threshold'])\n",
    "        self.identify_single_unique(features)\n",
    "        self.identify_collinear(features, selection_params['correlation_threshold'])\n",
    "        self.identify_zero_importance(features, labels, selection_params['eval_metric'], selection_params['task'])\n",
    "        self.identify_low_importance(selection_params['cumulative_importance'])\n",
    "        \n",
    "        # Find the number of features identified to drop\n",
    "        self.n_identified = len(set(list(chain(*list(self.removal_ops.values())))))\n",
    "        print('%d total features out of %d identified for removal.\\n' % (self.n_identified, pd.get_dummies(features).shape[1]))\n",
    "        \n",
    "    def check_identified(self):\n",
    "        \"\"\"Check the identified features before removal. Returns a set of the unique features identified.\"\"\"\n",
    "        \n",
    "        all_identified = set(list(chain(*list(self.removal_ops.values()))))\n",
    "        print('%d features identified for removal' % len(all_identified))\n",
    "        \n",
    "        return all_identified\n",
    "        \n",
    "    \n",
    "    def remove(self, data, methods):\n",
    "        \"\"\"\n",
    "        Remove the features from the data according to the specified methods.\n",
    "        \n",
    "        Parameters\n",
    "        --------\n",
    "            data : dataframe\n",
    "                Dataframe with features to remove\n",
    "            methods : 'all' or list of methods\n",
    "                If methods == 'all', any methods that have identified features will be used\n",
    "                Otherwise, only the specified methods will be used.\n",
    "                Can be one of ['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance']\n",
    "                \n",
    "        Return\n",
    "        --------\n",
    "            data : dataframe\n",
    "                Dataframe with identified features removed\n",
    "                \n",
    "        \n",
    "        Notes \n",
    "        --------\n",
    "            - This first one-hot encodes the categorical variables in accordance with the gradient boosting machine.\n",
    "            - Check the features that will be removed before transforming data!\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        \n",
    "        features_to_drop = []\n",
    "        \n",
    "        data = pd.get_dummies(data)\n",
    "        \n",
    "        if methods == 'all':\n",
    "            \n",
    "            print('{} methods have been run'.format(list(self.removal_ops.keys())))\n",
    "            \n",
    "            # Find the unique features to drop\n",
    "            features_to_drop = set(list(chain(*list(self.removal_ops.values()))))\n",
    "            \n",
    "        else:\n",
    "            # Iterate through the specified methods\n",
    "            for method in methods:\n",
    "                # Check to make sure the method has been run\n",
    "                if method not in self.removal_ops.keys():\n",
    "                    raise NotFittedError('%s method has not been run' % method)\n",
    "                    \n",
    "                # Append the features identified for removal\n",
    "                else:\n",
    "                    features_to_drop.append(self.removal_ops[method])\n",
    "        \n",
    "            # Find the unique features to drop\n",
    "            features_to_drop = set(list(chain(*features_to_drop)))\n",
    "            \n",
    "        # Remove the features and return the data\n",
    "        data = data.drop(columns = features_to_drop)\n",
    "        self.removed_features = features_to_drop\n",
    "        \n",
    "        print('Removed %d features' % len(features_to_drop))\n",
    "        return data\n",
    "    \n",
    "    def plot_missing(self):\n",
    "        \"\"\"Histogram of missing fraction in each feature\"\"\"\n",
    "        if self.record_missing is None:\n",
    "            raise NotImplementedError(\"Missing values have not been calculated. Run `identify_missing`\")\n",
    "        \n",
    "        self.reset_plot()\n",
    "        self.missing_stats.plot.hist(color = 'red', edgecolor = 'k', figsize = (6, 4), fontsize = 14)\n",
    "        plt.ylabel('Frequency', size = 18)\n",
    "        plt.xlabel('Missing Fraction', size = 18); plt.title('Missing Fraction Histogram', size = 18);\n",
    "        \n",
    "    \n",
    "    def plot_unique(self):\n",
    "        \"\"\"Histogram of number of unique values in each feature\"\"\"\n",
    "        if self.record_single_unique is None:\n",
    "            raise NotImplementedError('Unique values have not been calculated. Run `identify_single_unique`')\n",
    "        \n",
    "        self.reset_plot()\n",
    "        self.unique_stats.plot.hist(edgecolor = 'k', figsize = (6, 4), fontsize = 14)\n",
    "        plt.ylabel('Frequency', size = 18)\n",
    "        plt.xlabel('Unique Values', size = 18); plt.title('Unique Values Histogram', size = 18);\n",
    "        \n",
    "    \n",
    "    def plot_collinear(self):\n",
    "        \"\"\"\n",
    "        Heatmap of the features with correlations above the correlated threshold in the data.\n",
    "        \n",
    "        Notes\n",
    "        --------\n",
    "            - Not all of the plotted correlations are above the threshold because this plots\n",
    "            all the variables that have been idenfitied as having even one correlation above the threshold\n",
    "            - The features on the x-axis are those that will be removed. The features on the y-axis\n",
    "            are the correlated feature with those on the x-axis\n",
    "        \n",
    "        \"\"\"\n",
    "        \n",
    "        if self.record_collinear is None:\n",
    "            raise NotImplementedError('Collinear features have not been idenfitied. Run `identify_collinear`.')\n",
    "        \n",
    "        # Identify the correlations that were above the threshold\n",
    "        corr_matrix_plot = self.corr_matrix.loc[list(set(self.record_collinear['corr_feature'])), \n",
    "                                                list(set(self.record_collinear['drop_feature']))]\n",
    "\n",
    "        # Set up the matplotlib figure\n",
    "        f, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "        # Generate a custom diverging colormap\n",
    "        cmap = sns.diverging_palette(220, 10, as_cmap=True)\n",
    "\n",
    "        # Draw the heatmap with the mask and correct aspect ratio\n",
    "        sns.heatmap(corr_matrix_plot, cmap=cmap, center=0,\n",
    "                    linewidths=.25, cbar_kws={\"shrink\": 0.6})\n",
    "\n",
    "        ax.set_yticks([x + 0.5 for x in list(range(corr_matrix_plot.shape[0]))])\n",
    "        ax.set_yticklabels(list(corr_matrix_plot.index), size = int(160 / corr_matrix_plot.shape[0]));\n",
    "\n",
    "        ax.set_xticks([x + 0.5 for x in list(range(corr_matrix_plot.shape[1]))])\n",
    "        ax.set_xticklabels(list(corr_matrix_plot.columns), size = int(160 / corr_matrix_plot.shape[1]));\n",
    "        \n",
    "        plt.xlabel('Features to Remove', size = 8); plt.ylabel('Correlated Feature', size = 8)\n",
    "        plt.title(\"Correlations Above Threshold\", size = 14)\n",
    "        \n",
    "    def plot_feature_importances(self, threshold = None):\n",
    "        \"\"\"\n",
    "        Plots 15 most important features and the cumulative importance of features.\n",
    "        If `threshold` is provided, prints the number of features needed to reach `threshold` cumulative importance.\n",
    "\n",
    "        Parameters\n",
    "        --------\n",
    "        threshold : float, between 0 and 1 default = None\n",
    "            Threshold for printing information about cumulative importances\n",
    "\n",
    "        \"\"\"\n",
    "        \n",
    "        if self.record_zero_importance is None:\n",
    "            raise NotImplementedError('Feature importances have not been determined. Run `idenfity_zero_importance`')\n",
    "\n",
    "        self.reset_plot()\n",
    "        \n",
    "        # Make a horizontal bar chart of feature importances\n",
    "        plt.figure(figsize = (10, 6))\n",
    "        ax = plt.subplot()\n",
    "\n",
    "        # Need to reverse the index to plot most important on top\n",
    "        ax.barh(list(reversed(list(self.feature_importances.index[:15]))), \n",
    "                self.feature_importances['normalized_importance'].head(15), \n",
    "                align = 'center', edgecolor = 'k')\n",
    "\n",
    "        # Set the yticks and labels\n",
    "        ax.set_yticks(list(reversed(list(self.feature_importances.index[:15]))))\n",
    "        ax.set_yticklabels(self.feature_importances['feature'].head(15), size = 12)\n",
    "\n",
    "        # Plot labeling\n",
    "        plt.xlabel('Normalized Importance', size = 16); plt.title('Feature Importances', size = 18)\n",
    "        plt.show()\n",
    "\n",
    "        # Cumulative importance plot\n",
    "        plt.figure(figsize = (6, 4))\n",
    "        plt.plot(list(range(1, len(self.feature_importances) + 1)), self.feature_importances['cumulative_importance'], 'r-')\n",
    "        plt.xlabel('Number of Features', size = 14); plt.ylabel('Cumulative Importance', size = 14); \n",
    "        plt.title('Cumulative Feature Importance', size = 16);\n",
    "\n",
    "        if threshold:\n",
    "\n",
    "            # Index of minimum number of features needed for cumulative importance threshold\n",
    "            importance_index = np.min(np.where(self.feature_importances['cumulative_importance'] > threshold))\n",
    "            plt.vlines(x = importance_index + 1, ymin = 0, ymax = 1, linestyles='--', colors = 'blue')\n",
    "            plt.show()\n",
    "\n",
    "            print('%d features required for %0.2f of cumulative importance' % (importance_index + 1, threshold))\n",
    "\n",
    "        \n",
    "    def reset_plot(self):\n",
    "        plt.rcParams = plt.rcParamsDefault"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 features with greater than 0.90 missing values.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_missing(train, 0.9)\n",
    "fs.plot_missing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 features with a single unique value.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_single_unique(train)\n",
    "fs.plot_unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 features with a correlation greater than 0.99.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_collinear(train, 0.99)\n",
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35 features with a correlation greater than 0.90.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fs.identify_collinear(train, 0.90)\n",
    "fs.plot_collinear()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[98]\tvalid_0's auc: 0.740478\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[101]\tvalid_0's auc: 0.710965\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[90]\tvalid_0's auc: 0.695221\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[141]\tvalid_0's auc: 0.706424\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[70]\tvalid_0's auc: 0.72601\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[74]\tvalid_0's auc: 0.743911\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[67]\tvalid_0's auc: 0.712879\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[69]\tvalid_0's auc: 0.688675\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[168]\tvalid_0's auc: 0.654309\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[139]\tvalid_0's auc: 0.719488\n",
      "\n",
      "68 features with zero importance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_zero_importance(train, train_labels, eval_metric='auc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123 features required for 0.99 of cumulative importance\n"
     ]
    }
   ],
   "source": [
    "fs.plot_feature_importances(threshold = 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "116 features that do not contribute to cumulative importance of 0.99.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_low_importance(0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 features with greater than 0.90 missing values.\n",
      "\n",
      "5 features with a single unique value.\n",
      "\n",
      "31 features with a correlation greater than 0.95.\n",
      "\n",
      "Training Gradient Boosting Model\n",
      "\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[99]\tvalid_0's auc: 0.712925\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[98]\tvalid_0's auc: 0.685861\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[100]\tvalid_0's auc: 0.720052\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[138]\tvalid_0's auc: 0.702099\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[79]\tvalid_0's auc: 0.701611\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[89]\tvalid_0's auc: 0.730121\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[76]\tvalid_0's auc: 0.726643\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[126]\tvalid_0's auc: 0.716197\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[252]\tvalid_0's auc: 0.688444\n",
      "Training until validation scores don't improve for 100 rounds.\n",
      "Early stopping, best iteration is:\n",
      "[112]\tvalid_0's auc: 0.684804\n",
      "\n",
      "67 features with zero importance.\n",
      "\n",
      "152 features that do not contribute to cumulative importance of 0.95.\n",
      "\n",
      "172 total features out of 238 identified for removal.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs.identify_all(train, train_labels, {'missing_threshold': 0.9, 'correlation_threshold': 0.95, 'eval_metric': 'auc',\n",
    "                                      'task': 'classification', 'cumulative_importance': 0.95})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "172 features identified for removal\n"
     ]
    }
   ],
   "source": [
    "features_identified = fs.check_identified()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['FLAG_DOCUMENT_2',\n",
       " 'ORGANIZATION_TYPE_Military',\n",
       " 'LIVINGAREA_MODE',\n",
       " 'NAME_TYPE_SUITE_Other_A',\n",
       " 'ORGANIZATION_TYPE_Postal',\n",
       " 'FLAG_OWN_CAR_N',\n",
       " 'ORGANIZATION_TYPE_Business Entity Type 2',\n",
       " 'NAME_HOUSING_TYPE_Office apartment',\n",
       " 'NAME_INCOME_TYPE_State servant',\n",
       " 'WALLSMATERIAL_MODE_Monolithic']"
      ]
     },
     "execution_count": 350,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(features_identified)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['missing', 'single_unique', 'collinear', 'zero_importance', 'low_importance'] methods have been run\n",
      "Removed 172 features\n"
     ]
    }
   ],
   "source": [
    "train_removed = fs.remove(train, methods = 'all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feature_selector import FeatureSelector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = FeatureSelector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('../input/application_train.csv')\n",
    "train = train.sample(10000)\n",
    "train.to_csv('data/example.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
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}
